OpenRaiser/Intern-Atlas huggingface dataset:
Image Credibility Analysis with Effective Domain Transferred Deep Networks
Venue: arXiv cs.MM 2016
Year: 2016

Numerous fake images spread on social media today and can severely jeopardize the credibility of online content to public. In this paper, we employ deep networks to learn distinct fake image related features. In contrast to authentic images, fake images tend to be eye-catching and visually striking. Compared with traditional visual recognition tasks, it is extremely challenging to understand these psychologically triggered visual patterns in fake images. Traditional general image classification datasets, such as ImageNet set, are designed for feature learning at the object level but are not suitable for learning the hyper-features that would be required by image credibility analysis. In order to overcome the scarcity of training samples of fake images, we first construct a large-scale auxiliary dataset indirectly related to this task. This auxiliary dataset contains 0.6 million weakly-labeled fake and real images collected automatically from social media. Through an AdaBoost-like transfer learning algorithm, we train a CNN model with a few instances in the target training set and 0.6 million images in the collected auxiliary set. This learning algorithm is able to leverage knowledge from the auxiliary set and gradually transfer it to the target task. Experiments on a real-world testing set show that our proposed domain transferred CNN model outperforms several competing baselines. It obtains superiror results over transfer learning methods based on the general ImageNet set. Moreover, case studies show that our proposed method reveals some interesting patterns for distinguishing fake and authentic images.
Context Awareness in Next Generation of Mobile Core Networks
Venue: arXiv cs.NI 2016
Year: 2016

Context awareness is an important enabler for next generation of Mobile Core Networks (MCN). However there exist a number of challenges in this regard. For example how to develop a framework which 1) is able to generate context richer than what is available today; 2) allows reusability of context across the network; 3) provides a mechanism for exposing context to third parties; and 4) can bring together "big data" for mobile core network optimization. In this work, we introduce a context awareness framework addressing the aforementioned challenges but also taking into account the 3GPP standardization activities related to context awareness in MCN. Within this framework we propose Context Generation and Handling Function (CGHF) which generates rich context by processing information from various sources and then handles its distribution through an efficient publish subscribe mechanism. In addition we provide examples where context can be used to optimize control plane decision making. While the focus of this work is on the use of context for MCN, we still believe such context can be also used by applications (at the edge as well as in data centers) and third party services to improve their operations and providing new unforeseen services.
Reinforcement Learning with Unsupervised Auxiliary Tasks
Venue: arXiv cs.LG 2016
Year: 2016

Deep reinforcement learning agents have achieved state-of-the-art results by directly maximising cumulative reward. However, environments contain a much wider variety of possible training signals. In this paper, we introduce an agent that also maximises many other pseudo-reward functions simultaneously by reinforcement learning. All of these tasks share a common representation that, like unsupervised learning, continues to develop in the absence of extrinsic rewards. We also introduce a novel mechanism for focusing this representation upon extrinsic rewards, so that learning can rapidly adapt to the most relevant aspects of the actual task. Our agent significantly outperforms the previous state-of-the-art on Atari, averaging 880\% expert human performance, and a challenging suite of first-person, three-dimensional \emph{Labyrinth} tasks leading to a mean speedup in learning of 10$\times$ and averaging 87\% expert human performance on Labyrinth.
Common Reconstructions in the Successive Refinement Problem with Receiver Side Information
Venue: arXiv cs.IT 2016
Year: 2016

We study a variant of the successive refinement problem with receiver side information where the receivers require identical reconstructions. We present general inner and outer bounds for the rate region for this variant and present a single-letter characterization of the admissible rate region for several classes of the joint distribution of the source and the side information. The characterization indicates that the side information can be fully used to reduce the communication rates via binning; however, the reconstruction functions can depend only on the Gács-Körner common randomness shared by the two receivers. Unlike existing (inner and outer) bounds to the rate region of the general successive refinement problem, the characterization of the admissible rate region derived for several settings of the variant studied requires only one auxiliary random variable. Using the derived characterization, we establish that the admissible rate region is not continuous in the underlying source source distribution even though the problem formulation does not involve zero-error or functional reconstruction constraints.
Automatic Node Selection for Deep Neural Networks using Group Lasso Regularization
Venue: arXiv cs.CL 2016
Year: 2016

We examine the effect of the Group Lasso (gLasso) regularizer in selecting the salient nodes of Deep Neural Network (DNN) hidden layers by applying a DNN-HMM hybrid speech recognizer to TED Talks speech data. We test two types of gLasso regularization, one for outgoing weight vectors and another for incoming weight vectors, as well as two sizes of DNNs: 2048 hidden layer nodes and 4096 nodes. Furthermore, we compare gLasso and L2 regularizers. Our experiment results demonstrate that our DNN training, in which the gLasso regularizer was embedded, successfully selected the hidden layer nodes that are necessary and sufficient for achieving high classification power.
An Improved Integrality Gap for the Calinescu-Karloff-Rabani Relaxation for Multiway Cut
Venue: arXiv cs.DS 2016
Year: 2016

We construct an improved integrality gap instance for the Calinescu-Karloff-Rabani LP relaxation of the Multiway Cut problem. In particular, for $k \geqslant 3$ terminals, our instance has an integrality ratio of $6 / (5 + \frac{1}{k - 1}) - \varepsilon$, for every constant $\varepsilon > 0$. For every $k \geqslant 4$, our result improves upon a long-standing lower bound of $8 / (7 + \frac{1}{k - 1})$ by Freund and Karloff (2000). Due to Manokaran et al.'s result (2008), our integrality gap also implies Unique Games hardness of approximating Multiway Cut of the same ratio.
Multiple Access Technologies for cellular M2M Communications: An Overview
Venue: arXiv cs.IT 2016
Year: 2016

This paper reviews the multiple access techniques for machine-to-machine (M2M) communications in future wireless cellular networks. M2M communications aims at providing te communication infrastructure for the emerging Internet of Things (IoT), which will revolutionize the way we interact with our surrounding physical environment. We provide an overview of the multiple access strategies and explain their limitations when used for M2M communications. We show the throughput efficiency of different multiple access techniques when used in coordinated and uncoordinated scenarios. Non-orthogonal multiple access is also shown to support a larger number of devices compared to orthogonal multiple access techniques, especially in uncoordinated scenarios. We also detail the issues and challenges of different multiple access techniques to be used for M2M applications in cellular networks.
A Prudent-Precedence Concurrency Control Protocol for High Data Contention Database Enviornments
Venue: arXiv cs.DB 2016
Year: 2016

In this paper, we propose a concurrency control protocol, called the Prudent-Precedence Concurrency Control (PPCC) protocol, for high data contention database environments. PPCC is prudently more aggressive in permitting more serializable schedules than two-phase locking. It maintains a restricted precedence among conflicting transactions and commits the transactions according to the serialization order established in the executions. A detailed simulation model has been constructed and extensive experiments have been conducted to evaluate the performance of the proposed approach. The results demonstrate that the proposed algorithm outperforms the two-phase locking and optimistic concurrency control in all ranges of system workload.
Towards Adaptive Compliance
Venue: arXiv cs.SE 2016
Year: 2016

Mission critical software is often required to comply with multiple regulations, standards or policies. Recent paradigms, such as cloud computing, also require software to operate in heterogeneous, highly distributed, and changing environments. In these environments, compliance requirements can vary at runtime and traditional compliance management techniques, which are normally applied at design time, may no longer be sufficient. In this paper, we motivate the need for adaptive compliance by illustrating possible compliance concerns determined by runtime variability. We further motivate our work by means of a cloud computing scenario, and present two main contributions. First, we propose and justify a process to support adaptive compliance that ex- tends the traditional compliance management lifecycle with the activities of the Monitor-Analyse-Plan-Execute (MAPE) loop, and enacts adaptation through re-configuration. Second, we explore the literature on software compliance and classify existing work in terms of the activities and concerns of adaptive compliance. In this way, we determine how the literature can support our proposal and what are the open research challenges that need to be addressed in order to fully support adaptive compliance.
Achievable Uplink Rates for Massive MIMO with Coarse Quantization
Venue: arXiv cs.IT 2016
Year: 2016

The high hardware complexity of a massive MIMO base station, which requires hundreds of radio chains, makes it challenging to build commercially. One way to reduce the hardware complexity and power consumption of the receiver is to lower the resolution of the analog-to-digital converters (ADCs). We derive an achievable rate for a massive MIMO system with arbitrary quantization and use this rate to show that ADCs with as low as 3 bits can be used without significant performance loss at spectral efficiencies around 3.5 bpcu per user, also under interference from stronger transmitters and with some imperfections in the automatic gain control.
Optimal Dynamic Coverage Infrastructure for Large-Scale Fleets of Reconnaissance UAVs
Venue: arXiv cs.AI 2016
Year: 2016

Current state of the art in the field of UAV activation relies solely on human operators for the design and adaptation of the drones' flying routes. Furthermore, this is being done today on an individual level (one vehicle per operators), with some exceptions of a handful of new systems, that are comprised of a small number of self-organizing swarms, manually guided by a human operator. Drones-based monitoring is of great importance in variety of civilian domains, such as road safety, homeland security, and even environmental control. In its military aspect, efficiently detecting evading targets by a fleet of unmanned drones has an ever increasing impact on the ability of modern armies to engage in warfare. The latter is true both traditional symmetric conflicts among armies as well as asymmetric ones. Be it a speeding driver, a polluting trailer or a covert convoy, the basic challenge remains the same -- how can its detection probability be maximized using as little number of drones as possible. In this work we propose a novel approach for the optimization of large scale swarms of reconnaissance drones -- capable of producing on-demand optimal coverage strategies for any given search scenario. Given an estimation cost of the threat's potential damages, as well as types of monitoring drones available and their comparative performance, our proposed method generates an analytically provable strategy, stating the optimal number and types of drones to be deployed, in order to cost-efficiently monitor a pre-defined region for targets maneuvering using a given roads networks. We demonstrate our model using a unique dataset of the Israeli transportation network, on which different deployment schemes for drones deployment are evaluated.
Relational Multi-Manifold Co-Clustering
Venue: arXiv cs.LG 2016
Year: 2016

Co-clustering targets on grouping the samples (e.g., documents, users) and the features (e.g., words, ratings) simultaneously. It employs the dual relation and the bilateral information between the samples and features. In many realworld applications, data usually reside on a submanifold of the ambient Euclidean space, but it is nontrivial to estimate the intrinsic manifold of the data space in a principled way. In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces. To achieve this, we develop a novel co-clustering algorithm called Relational Multi-manifold Co-clustering (RMC) based on symmetric nonnegative matrix tri-factorization, which decomposes the relational data matrix into three submatrices. This method considers the intertype relationship revealed by the relational data matrix, and also the intra-type information reflected by the affinity matrices encoded on the sample and feature data distributions. Specifically, we assume the intrinsic manifold of the sample or feature space lies in a convex hull of some pre-defined candidate manifolds. We want to learn a convex combination of them to maximally approach the desired intrinsic manifold. To optimize the objective function, the multiplicative rules are utilized to update the submatrices alternatively. Besides, both the entropic mirror descent algorithm and the coordinate descent algorithm are exploited to learn the manifold coefficient vector. Extensive experiments on documents, images and gene expression data sets have demonstrated the superiority of the proposed algorithm compared to other well-established methods.
FPGA Implementation of a Scalable and Run-Time Adaptable Multi-Standard Packet Detector
Venue: arXiv cs.NI 2016
Year: 2016

This paper describes a step by step approach for implementing a scalable and run-time adaptable multi-standard packet detector for orthogonal frequency divisional multiplexing (OFDM) based communication standards. The paper briefly describes considerations and design choices in making a modular system block with generic control supporting rapid prototyping and implementation of preamble-based packet detectors. The results were generated through implementation on a Xilinx Virtex-6 FPGA with a MicroBlaze processor instantiated to provide run-time control and adaptability.
Applying Software Craftsmanship Practices to a Scrum Project: an Experience Report
Venue: arXiv cs.SE 2016
Year: 2016

The Software Craftsmanship manifesto has defined values and principles that software development teams should follow to deliver quality software that fulfills functional and non-functional requirements without dealing with high amounts of technical debt. Software craftsmanship approach to software development prioritizes technical practices in order to provide a clean code base. This work analyzes a set of practices that can be applied to a Scrum project that aims to incorporate Software Craftsmanship values. The process implementation described may be a useful contribution for software development teams who also intend to implement Software Craftsmanship on their projects.
Generalized LR parsing and the shuffle operator
Venue: arXiv cs.FL 2016
Year: 2016

We adapt Tomita's Generalized LR algorithm to languages generated by context-free grammars enriched with a shuffle operator. The change involves extensions to the underlying handle-finding finite automaton, construction of parser tables, and the necessary optimizations in constructing a deterministic parser. Our system is motivated by an application from artificial intelligence plan recognition. We argue for the correctness of the system, and discuss future extensions of this work.
Generative One-Class Models for Text-based Person Retrieval in Forensic Applications
Venue: arXiv cs.CV 2016
Year: 2016

Automatic forensic image analysis assists criminal investigation experts in the search for suspicious persons, abnormal behaviors detection and identity matching in images. In this paper we propose a person retrieval system that uses textual queries (e.g., "black trousers and green shirt") as descriptions and a one-class generative color model with outlier filtering to represent the images both to train the models and to perform the search. The method is evaluated in terms of its efficiency in fulfilling the needs of a forensic retrieval system: limited annotation, robustness, extensibility, adaptability and computational cost. The proposed generative method is compared to a corresponding discriminative approach. Experiments are carried out using a range of queries in three different databases. The experiments show that the two evaluated algorithms provide average retrieval performance and adaptable to new datasets. The proposed generative algorithm has some advantages over the discriminative one, specifically its capability to work with very few training samples and its much lower computational requirements when the number of training examples increases.
Fuzzy Statistical Matrices for Cell Classification
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we generalize image (texture) statistical descriptors and propose algorithms that improve their efficacy. Recently, a new method showed how the popular Co-Occurrence Matrix (COM) can be modified into a fuzzy version (FCOM) which is more effective and robust to noise. Here, we introduce new fuzzy versions of two additional higher order statistical matrices: the Run Length Matrix (RLM) and the Size Zone Matrix (SZM). We define the fuzzy zones and propose an efficient algorithm to compute the descriptors. We demonstrate the advantage of the proposed improvements over several state-of-the-art methods on three tasks from quantitative cell biology: analyzing and classifying Human Epithelial type 2 (HEp-2) cells using Indirect Immunofluorescence protocol (IFF).
Polynomial self-stabilizing algorithm and proof for a 2/3-approximation of a maximum matching
Venue: arXiv cs.DC 2016
Year: 2016

We present the first polynomial self-stabilizing algorithm for finding a $\frac23$-approximation of a maximum matching in a general graph. The previous best known algorithm has been presented by Manne \emph{et al.} \cite{ManneMPT11} and has a sub-exponential time complexity under the distributed adversarial daemon \cite{Coor}. Our new algorithm is an adaptation of the Manne \emph{et al.} algorithm and works under the same daemon, but with a time complexity in $O(n^3)$ moves. Moreover, our algorithm only needs one more boolean variable than the previous one, thus as in the Manne \emph{et al.} algorithm, it only requires a constant amount of memory space (three identifiers and $two$ booleans per node).
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
Venue: arXiv cs.CV 2016
Year: 2016

Human action recognition is an important task in computer vision. Extracting discriminative spatial and temporal features to model the spatial and temporal evolutions of different actions plays a key role in accomplishing this task. In this work, we propose an end-to-end spatial and temporal attention model for human action recognition from skeleton data. We build our model on top of the Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM), which learns to selectively focus on discriminative joints of skeleton within each frame of the inputs and pays different levels of attention to the outputs of different frames. Furthermore, to ensure effective training of the network, we propose a regularized cross-entropy loss to drive the model learning process and develop a joint training strategy accordingly. Experimental results demonstrate the effectiveness of the proposed model,both on the small human action recognition data set of SBU and the currently largest NTU dataset.
Navigational Rule Derivation: An algorithm to determine the effect of traffic signs on road networks
Venue: arXiv cs.AI 2016
Year: 2016

In this paper we present an algorithm to build a road network map enriched with traffic rules such as one-way streets and forbidden turns, based on the interpretation of already detected and classified traffic signs. Such algorithm helps to automatize the elaboration of maps for commercial navigation systems. Our solution is based on simulating navigation along the road network, determining at each point of interest the visibility of the signs and their effect on the roads. We test our approach in a small urban network and discuss various ways to generalize it to support more complex environments.
Stratified Knowledge Bases as Interpretable Probabilistic Models (Extended Abstract)
Venue: arXiv cs.AI 2016
Year: 2016

In this paper, we advocate the use of stratified logical theories for representing probabilistic models. We argue that such encodings can be more interpretable than those obtained in existing frameworks such as Markov logic networks. Among others, this allows for the use of domain experts to improve learned models by directly removing, adding, or modifying logical formulas.
Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks
Venue: arXiv cs.CL 2016
Year: 2016

Curriculum Learning emphasizes the order of training instances in a computational learning setup. The core hypothesis is that simpler instances should be learned early as building blocks to learn more complex ones. Despite its usefulness, it is still unknown how exactly the internal representation of models are affected by curriculum learning. In this paper, we study the effect of curriculum learning on Long Short-Term Memory (LSTM) networks, which have shown strong competency in many Natural Language Processing (NLP) problems. Our experiments on sentiment analysis task and a synthetic task similar to sequence prediction tasks in NLP show that curriculum learning has a positive effect on the LSTM's internal states by biasing the model towards building constructive representations i.e. the internal representation at the previous timesteps are used as building blocks for the final prediction. We also find that smaller models significantly improves when they are trained with curriculum learning. Lastly, we show that curriculum learning helps more when the amount of training data is limited.
Quantized neural network design under weight capacity constraint
Venue: arXiv cs.LG 2016
Year: 2016

The complexity of deep neural network algorithms for hardware implementation can be lowered either by scaling the number of units or reducing the word-length of weights. Both approaches, however, can accompany the performance degradation although many types of research are conducted to relieve this problem. Thus, it is an important question which one, between the network size scaling and the weight quantization, is more effective for hardware optimization. For this study, the performances of fully-connected deep neural networks (FCDNNs) and convolutional neural networks (CNNs) are evaluated while changing the network complexity and the word-length of weights. Based on these experiments, we present the effective compression ratio (ECR) to guide the trade-off between the network size and the precision of weights when the hardware resource is limited.